Evaluation of the innovation transformation efficiency of Chinese listed SRDI enterprises based on an improved dynamic three-stage network SBM-DEA model

Specialised, refined, distinctive, and innovative (SRDI) enterprises hold profound strategic significance for advancing China’s high-quality economic development. However, SRDI enterprises face challenges in translating innovation outcomes into practical value, and their innovation transformation efficiency (ITE) has yet to be fully explored. Therefore, this study aims to identify critical weaknesses of SRDI enterprises and to formulate adaptive strategies for enhancing their ITE. A novel evaluation system is proposed, grounded in the synergy among R&D applications, R&D authorisations, and commercial transformation. This system is employed to assess the ITE by adopting an improved dynamic three-stage network slack-based measure data envelopment analysis (SBM-DEA) model. Analysis of SRDI enterprises from 2019 to 2023 reveals three main findings: (1) The overall ITE remains low, attributable to the fact that innovation R&D efficiency (IRDE) and commercial transformation efficiency (CTE) are clustered around low mean values in most provinces. (2) Over the five-year period, IRDE is observed to be stronger than CTE, with the latter demonstrating the weakest results. (3) Significant regional heterogeneity exists, with SRDI enterprises in the eastern and western regions exhibiting higher ITE than those in other regions. These findings underscore the need for tailored policy recommendations for SRDI enterprises in different regions.

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Publication Details

Journal
Technology Analysis and Strategic Management
Published
2026-10-15
DOI
https://doi.org/10.1080/09537325.2026.2742996
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
0.00

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article

Evaluation of the innovation transformation efficiency of Chinese listed SRDI enterprises based on an improved dynamic three-stage network SBM-DEA model

Meijuan Li, Hongcheng Xu, Ning Yuan, Wei Yang
Technology Analysis and Strategic Management
Efficiency Analysis Using DEA
article

Evaluation of the innovation transformation efficiency of Chinese listed SRDI enterprises based on an improved dynamic three-stage network SBM-DEA model

Meijuan Li, Hongcheng Xu, Ning Yuan, Wei Yang
article en

Abstract

Specialised, refined, distinctive, and innovative (SRDI) enterprises hold profound strategic significance for advancing China’s high-quality economic development. However, SRDI enterprises face challenges in translating innovation outcomes into practical value, and their innovation transformation efficiency (ITE) has yet to be fully explored. Therefore, this study aims to identify critical weaknesses of SRDI enterprises and to formulate adaptive strategies for enhancing their ITE. A novel evaluation system is proposed, grounded in the synergy among R&D applications, R&D authorisations, and commercial transformation. This system is employed to assess the ITE by adopting an improved dynamic three-stage network slack-based measure data envelopment analysis (SBM-DEA) model. Analysis of SRDI enterprises from 2019 to 2023 reveals three main findings: (1) The overall ITE remains low, attributable to the fact that innovation R&D efficiency (IRDE) and commercial transformation efficiency (CTE) are clustered around low mean values in most provinces. (2) Over the five-year period, IRDE is observed to be stronger than CTE, with the latter demonstrating the weakest results. (3) Significant regional heterogeneity exists, with SRDI enterprises in the eastern and western regions exhibiting higher ITE than those in other regions. These findings underscore the need for tailored policy recommendations for SRDI enterprises in different regions.

Technology Analysis and Strategic Management
Fuzhou University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Fujian Province
Industry, innovation and infrastructure, Decent work and economic growth
Openalex Percentile: Top 10%
Efficiency Analysis Using DEA
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